Method and computer for determination of a training function for generating annotated training images

ABSTRACT

In a method and a computer for determining a training function in order to generate annotated training images, a training image and training-image information are provided to a computer, together with an isolated item of image information that is independent of the training image. A first calculation is made in the computer by applying an image-information-processing first function to the isolated item of image information, and a second calculation is made by applying an image-information-processing second function to the training image. Adjustments to the first and second functions are made based on these calculation results, from which a determination of a training function is then made in the computer.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention concerns a method for determining a trainingfunction for generating annotated training images, as well as a computerfor implementing such a method.

Description of the Prior Art

In machine-based image processing and image classification, there hasbeen great progress in the use of so-called “deep learning” algorithms,wherein the algorithms have been trained using very large volumes ofdata. Examples of such algorithms are “deep artificial neural networks”(“deep ANNs”) or “deep convolutional artificial neuronal networks”(“deep convolutional ANNs”). Very large volumes of data are provided bypublically accessible databases, in particular image databases such as“ImageNet”.

The use of such algorithms in machine-based image processing or in imageclassification can achieve better quality and performance than a human.This means, for example, that for the same task, machine-based imageclassifiers produce a lower error rate than an average human classifierand at the same time, the image classification is performed morequickly.

These technologies can also be used in the field of medical imageanalysis, for example for the detection and segmentation of organs,anatomical structures, lesions and other pathologies that are usuallydetectable with “computer-aided diagnosis” (CAD).

Although great advances in medicine can be expected from the use of“deep learning”, the specific use of artificial neural networks remainsa great challenge since annotated training data usually are notavailable in sufficient amounts. Annotation can be provided by marking aregion by a square window or segmentation or by text and/or numericalcomments on the image, which, for example, indicate a specific diagnosisin the image.

Annotation of medical image data is a tedious and monotonous task butnevertheless has to be performed by healthcare professionals, forexample a radiologist. This makes the generation of annotated medicalimage data particularly time-consuming and costly.

From the publication Ian J. GOODFELLOW, “Generative AdversarialNetworks”, arxiv 1406.2661 (2014) is a method known for the generationof image data that appears similar to a defined training volume ofimages by means of “generative adversarial networks” (“GAN”). Herein,two artificial neural networks with contradictory tasks are used.However, this method is not able to generate annotated training imagedata.

SUMMARY OF THE INVENTION

An object of the present invention is to provide a method for generatinga training function for the rapid and inexpensive generation ofannotated image data for training self-learning algorithms.

The inventive method is described below, but features, advantages andalternative embodiments mentioned with regard to the method areapplicable to the other aspects of the invention. The relevantfunctional features of the method are embodied as appropriate physicalmodules in the computer.

The inventive method includes a first reception of a training image, andan item of training-image information, via an interface of a computer,wherein the training-image information is image information belonging tothe training image. The inventive method furthermore includes a secondreception of an isolated item of image information via the interface,wherein the isolated item of image information is independent of thetraining image. A first calculation is made in the computer of a firstresult of an image-information-processing first function when applied tothe isolated item of image information. Furthermore, a secondcalculation is made in the computer of a second result of animage-information-processing second function when applied to thetraining image. Furthermore, the computer makes an adjustment of aparameter of the image-information-processing first function and/or theimage-processing second function based on at least the first result andthe second result. Furthermore, the computer makes a determination of atraining function based on the image-information-processing firstfunction. The computer applies the training function to an item of imageinformation, so as to generate an associated image, which is provided asan output from the computer.

The invention is based on the insight that the use of animage-information-processing second function as the basis of thetraining function enables training images to be generated not only fromrandom data, but also from data with an information content. At the sametime, this procedure generates annotated training data because inputinto such a training function is simultaneously the annotation of theoutput of the training function.

The use of the first result and the second result for the adjustment ofthe parameters of the first and second function enables a particularlygood evaluation of the progress of the training and hence thedetermination of the training function in very few iteration steps.

According to a further embodiment of the invention, the first functionand/or the second function are defined by an artificial neural networkand the parameters of the artificial neural network furthermore comprisethe edge weights of the artificial neural network. A function defined byan artificial neural network is particularly suitable for adjustment totraining data.

According to a further embodiment of the invention, the adjustment ofthe edge weights is performed by minimizing a cost function by means ofbackpropagation. The use of a cost function enables the efficientacquisition of the difference of the output of an artificial neuralnetwork from the comparison value. The cost function can be minimizedparticularly quickly by backpropagation.

According to a further embodiment of the invention, an item of imageinformation of an image comprises segmentation of the image into atleast one image region. Manual segmentation of training data isparticularly time-consuming and the determination of a training functionfor this task significantly accelerates the creation of annotatedtraining data.

According to a further embodiment of the invention, the item of imageinformation of the image is a variable that assesses whether a definedobject or number of defined objects, is/are depicted in the image. Theitem of image information can also be a property of an object. Simpleimage information of this kind requires very few training images anditems of training image information for the determination of thetraining function.

According to a further embodiment of the invention, theimage-information-processing first function is a generator functionthat, when applied to the item of image information, generates anassociated image as an output. The image-processing second function is aclassification function that, when applied to the image, generates anassociated item of image information as an output. The first result is acalculated image and the second result is a first item of calculatedimage information. The training function is theimage-information-processing first function. The method furthermoreincludes a third calculation of a second item of calculated imageinformation by applying the image-processing function to the calculatedimage. This choice of the first and the second function causes thetraining function to be obtained particularly simply and quickly fromthe second function. Furthermore, the image-processing second functioncan already be used as a pretrained function for the training with theannotated image data calculated by the training function.

According to a further embodiment of the invention, the first item ofcalculated image information is an estimation of a first probability ofthe training image being contained in a set of training images and, inaddition, the second item of calculated image information is anestimation of a second probability of the calculated image beingcontained in a set of training images. The use of image information ofthis kind enables particularly good determination of a training functionthat generates images similar to the training images.

According to a further embodiment of the invention, the cost function isbased on at least a first difference of the first calculated imageinformation from the training-image information. As a result of theinclusion of this difference in the cost function, the second functionis trained to calculate an item of image information of an input imageparticularly accurately.

According to a further embodiment of the invention, the cost function isfurthermore based on at least a second difference of the second item ofcalculated image information from the isolated item of imageinformation. As a result of the inclusion of this difference in the costfunction, the second function is trained to calculate an item of imageinformation of an input image particularly accurately, andsimultaneously the first function is trained to generate suitablecalculated images from isolated items of image information.

According to a further embodiment of the invention, theimage-information-processing first function is an informationautoencoder that when applied to a first item of image information,generates a second item of image information as an output. Theimage-processing second function is an image autoencoder that whenapplied to a first image, generates a second image as an output.Furthermore, the central layer of the information autoencoder and thecentral layer of the image autoencoder have the same number of centralnodes. The first result corresponds to first node values, wherein thefirst node values are the values of the nodes of the central layer ofthe information autoencoder when the isolated item of image informationis the input value of the information autoencoder. The second resultcorresponds to second node values, wherein the second node values arethe values of the nodes of the central layer of the image autoencoderwhen the training image is the input value of the image autoencoder. Themethod furthermore includes a fourth calculation of third node values,wherein the third node values are the values of the nodes of the centrallayer of the information autoencoder when the training-image informationis the input value of the information autoencoder. In this case, thetraining function is composed of the first part of the informationautoencoder and the second part of the image autoencoder. The use of animage autoencoder and an information autoencoder enables the parametersof the first function and/or the second function to be determinedparticularly quickly since, with an autoencoder, the predicted outputfor each input value is known since the predicted output is identical tothe input value. Furthermore, the node values of the central layers ofthe image autoencoder and the information autoencoder can be used toestablish a relationship between an image and an item of imageinformation when the central layers are of the same size.

According to a further embodiment of the invention, a distance betweenthe first node values and the second node values makes a negativecontribution to the cost function, and a distance between the secondnode values and the third node values makes a positive contribution tothe cost function. When the image autoencoder is applied to a trainingimage and an information autoencoder to the associated training-imageinformation, such a choice of cost function and the adjustment of theparameters of the first and the second function based thereupon causesthe node values of the central layer to be particularly similar, butsimultaneously, when the image autoencoder is applied to a trainingimage and the information autoencoder to an isolated item of imageinformation, the node values have a particularly large distance.Therefore, image information can be assigned particularly well toassociated images and vice versa.

According to a further embodiment of the invention, the trainingfunction generates an image as an output from the item of imageinformation as an input value, such that the item of image informationis used as an input value of the information autoencoder. The nodevalues of the central layer of the information autoencoder aretransferred to the node values of the central layer of the imageautoencoder and the output of the training function corresponds to theresulting output of the image autoencoder. This choice of trainingfunction and the similarity of the central node values of images andassociated image information enable annotated training data to begenerated particularly simply.

Furthermore, the invention relates to a function-determining computerthat includes the following.

An interface is configured for the first reception of a training image,and an item of training-image information, wherein the training-imageinformation is image information for the training image. The interfaceis also configured for the second reception of an isolated item of imageinformation, wherein the isolated item of image information isindependent of the training image. A processor is configured for thefirst calculation of a first result of an image-information-processingfirst function when applied to the isolated item of image information,and for the second calculation of a second result of an image-processingsecond function when applied to the training image. The processor isalso configured for the adjustment of a parameter of theimage-information-processing first function and/or the image-processingsecond function based on at least the first result and/or the secondresult. The processor is also configured for determining a trainingfunction based on the image-information-processing first function that,when applied to an item of image information, generates an associatedimage as an output of the computer.

Such a function-determining computer is designed to implement theabove-described method according to the invention, and the embodimentsthereof.

The present invention also encompasses a non-transitory,computer-readable data storage medium encoded with programminginstructions, said storage medium being loadable into a computer andsaid programming instructions then causing said computer to implementthe method in accordance with the invention, as described immediatelyabove.

It is furthermore the object of the present invention to generateannotated image data for training self-learning algorithms in a quickand cost-effective manner.

To achieve this object, in a further embodiment of the inventiontraining data are generated according to the method described above, andannotated training data are generated by applying the training functionto an item of input-image information by the computer (the processorthereof).

Furthermore, the invention can relate to a data generator that includesthe interface and the processor of the function-determining computer,wherein the computing unit is furthermore configured to generateannotated training data by applying the training function to an item ofinput-image information.

Such a data generator is designed to implement the above-describedmethod according to the invention and the embodiments thereof.

The present invention also encompasses a non-transitory,computer-readable data storage medium encoded with programminginstructions, said storage medium being loadable into a computer andsaid programming instructions then causing said computer to implementthe method in accordance with the invention, as described immediatelyabove.

An image is a two-dimensional arrangement of two-dimensional pixels oran N-dimensional arrangement of N-dimensional voxels, wherein N isgreater than or in particular equal to 3. In this case, a pixel or avoxel comprises at least one intensity value. In this case, an intensityvalue can be an overall intensity or an intensity of one color channelout of a number of color channels.

An item of image information characterizes an image, a part of an imageor an object depicted in the image. An item of image information can bethe presence or absence of a defined object in the image. Furthermore,an item of image information can be the probability of an imageoriginating from a distribution of training images or being similar to aset of images. Furthermore, an item of image information can be a maskdefined by a region of the image. Furthermore, an item of imageinformation can be segmentation of an image into one or more regions.Herein, a mask defines segmentation of the image into precisely tworegions, wherein the first region corresponds to the mask and the secondregion to the parts of the image outside the mask.

An isolated item of image information is independent of a training imagewhen the isolated item of image information is not an item of imageinformation from the training image. An isolated item of imageinformation independent of a training image and training-imageinformation can be an item of image information that was generatedsynthetically. A synthetically generated item of image information is inparticular not extracted from an associated image. However, it can alsobe an item of image information from an image other than the trainingimage.

An image-processing function is a function that obtains as an inputvalue at least one image, in particular a two-dimensional image or athree-dimensional image, and converts it into an output. In this case,the output can furthermore depend upon a set of parameters of theimage-processing function. In addition to the image, the input value ofthe image-processing function can include further variables.Image-processing functions are in particular “convolutional artificialneural networks”.

An image-information-processing function is a function that obtains, asan input value, an item of image information and converts it into anoutput. In this case, the output can furthermore depend upon a set ofparameters of the image-information-processing function. In addition tothe image information, the input value of theimage-information-processing function can also include furthervariables; in particular the input value can be a set of randomvariables.

An autoencoder is an artificial neural network constructed from layers,which depicts an input value on an output similar to the input value. Inthis case, the autoencoder comprises at least one input layer, an outputlayer with the same number of nodes as the input layer and a centrallayer between the input layer and the output layer with fewer nodes thanthe input layer. The nodes of the input layer are assigned to the inputdata in the same way as the assignment of the nodes of the output layerto the output data. The autoencoder can include further layers,furthermore, the autoencoder can be constructed symmetrically about thecentral layer. The lower number of nodes in the central layer comparedto the input and output layer results in the compression of the inputdata and decompression of the compressed input data to form the outputdata. Therefore, adjustment of at least the edge weights using trainingdata enables an autoencoder to learn a compression method and adecompression method.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic depiction of a method from the prior art for thedetermination of a training function.

FIG. 2 is a schematic depiction of a first exemplary embodiment of amethod for the determination of a training function.

FIG. 3 is a schematic depiction of a second exemplary embodiment of amethod for the determination of a training function.

FIG. 4 is a schematic depiction of a third exemplary embodiment of amethod for the determination of a training function.

FIG. 5 is a schematic depiction of the training function in the thirdexemplary embodiment of a method for the determination of a trainingfunction.

FIG. 6 depicts an artificial neural network.

FIG. 7 is a flowchart of first and second exemplary embodiments of themethod for the determination of a training function in accordance withthe invention.

FIG. 8 is a flowchart of a third exemplary embodiment of the method forthe determination of a training function.

FIG. 9 shows a function-determining computer for the determination of atraining function, designed to implement the method according to theinvention.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

FIG. 1 is a schematic depiction of a method for the determination of atraining function from the prior art. This method is described in thepublication: Ian J. GOODFELLOW, “Generative Adversarial Networks”, arxiv1406.2661 (2014).

The method is based on a set x1, x2, . . . , xN of training imagesstored in a database TI. An image-processing second function D isapplied to a training image x from the set x1, x2, . . . , xN oftraining images, wherein the image-processing second function D depictsan image x on a probability D_x of the training image x being containedin the set x1, x2, . . . , xN of training. Furthermore, adata-processing function G is applied to a vector r of random numbersand generates a calculated image G_r, wherein the vector r of randomnumbers is generated by a random-number generator RDG. Theimage-processing second function D is also applied to the calculatedimage G_r and estimates the probability D_G_r of the image G_r beingselected from the set x1, x2, . . . , xN of training data.

Furthermore, two cost functions, which are dependent upon D_G_r and D_x,are minimized by the adjustment of parameters of the first function Gand the second function D. A suitable choice of cost function causes thefirst function G to be trained such that the image-processing secondfunction D is not able to decide whether G_x is selected from the setx1, x2, . . . , xN of training data, i.e. D_G_x=0.5.

FIG. 2 shows a first exemplary embodiment of a method for determining atraining function. Herein, the image-information-processing firstfunction G and the image-processing second function D are each embodiedas an artificial neural network ANN and the parameters of the firstfunction G and the second function D are defined by the edge weights ofthe artificial neural network ANN. However, the first function G and thesecond function D can also be defined by other combinations of basicfunctions defined by parameters, for example as a combination of aFourier basis or as a combination of polynomials.

In this exemplary embodiment, an item of image information is defined bya binary variable that specifies whether an image contains a predefinedfeature, in this example a fracture in a bone depicted in an X-rayimage. It is also possible to use other predefined features, other itemsof image information and other images.

In this exemplary embodiment, a training database TD comprises trainingimages x1, x2, . . . , xN of human bones with associated training imageinformation i1, i2, . . . , iN. Here, the items of training imageinformation i1, i2, . . . , iN are in each case a binary variable, whichindicates whether the bone depicted in the associated training image hasa fracture. A training image x is selected from the training images x1,x2, . . . , xN and the second function D is applied thereto. In thisexemplary embodiment, the first item of calculated image information D_xis a two-dimensional vector with entries between 0 and 1, wherein thefirst vector entry indicates the degree of probability of the trainingimage x being a real image or an image from the training database TD andwherein the second vector entry indicates the degree of probability ofthe depicted bone having a bone fracture.

Furthermore, in the exemplary embodiment shown, a generator IIGgenerates a pair consisting of a random vector and an isolated item ofimage information y, wherein the isolated item of image information y isalso a binary variable. In this example, a random vector is an imagewith pixels the intensity of which is randomly distributed in accordancewith a uniform distribution between a minimum value and a maximum value.However, the intensity of the pixels can also be distributed inaccordance with another distribution, for example in accordance with anormal distribution. The first function G generates a calculated imageG_y from the random vector and the isolated item of image information y.The second function D is applied to this calculated image G_y; in thisexemplary embodiment, the second item of calculated image informationD_G_y is a two-dimensional vector with entries between 0 and 1, whereinthe first entry indicates the degree of probability of the calculatedimage G_y being contained in the training database TD and wherein thesecond entry indicates the degree of probability of the depicted bonehaving a bone fracture.

In this exemplary embodiment, the parameters of the first function G andthe second function D are adjusted by minimizing the cost functionsK_(G) and K_(D) by backpropagation. Those skilled in the art arefamiliar with backpropagation for artificial neural networks andtherefore a more detailed description is not necessary herein. The costfunction is in each case based on the first component of the firstcalculated image function D_x and the second item of calculated imageinformation D_G_y, wherein here the contributions to the cost functionare defined by the binary cross entropy

BCE(z,z′)∝z′·log(z)+(1−z′)·log(1−z)

wherein z is the calculated value of the variable and z′ is the actualvalue of the variable. Therefore, the cost functions are obtained as

K _(D)=BCE(D_x,1)+BCE(D_G_y,0)∝ log(D_x)+log(1−D_G_y)

K _(G)=BCE(D_G_y,1)∝ log(D_G_y)

wherein in each case only the first component of the first calculatedimage information D_x and the second item of calculated imageinformation D_G_y is used. Therefore, the two cost functions K_(D),K_(G) have alternating components so that, when both cost functions areminimized, the first function G and the second function D are trained toovercome one another. Other cost functions K_(D), K_(G) having thisproperty are also possible.

An optional comparison of the image information i of the training imagex and the second component of the first calculated image information D_xby means of a comparison function C1 depicted in this exemplaryembodiment enables the amount of the difference ε1 between the variablesto be calculated. The amount of the difference ε1 makes a positivecontribution to the cost function KD for the second function D; inparticular this cost function KD increases as the amount of thedifference ε1 increases, in particular the contribution to the costfunction can also be defined by binary cross entropy so thatminimization of the cost function KD also causes the amount of thedifference ε1 to be minimized.

FIG. 3 is a schematic depiction of a second exemplary embodiment of amethod for the determination of a training function. This secondexemplary embodiment includes the same elements as the first exemplaryembodiment of the method in accordance with FIG. 2, so reference is madeto the description of the schematic depiction of FIG. 2 with respect toelements that are the same.

In this exemplary embodiment, an item of image information of an imageis a segmentation of a region of interest, here a tumor in an image ofthe tumor. In this exemplary embodiment, a segmentation of an image isdefined in that a pixel that can be considered to be part of the tumoris assigned the value 1 in the segmentation. A pixel that cannot beconsidered to be part of the tumor is assigned the value 0 in thesegmentation. Such an item of image information can equivalently also beunderstood to be a mask of the tumor.

In this exemplary embodiment, a training database TD includes trainingimages x1, x2, . . . , xN with associated segmentations i1, i2, . . . ,iN. A training image x selected from the training images x1, x2, . . . ,xN and the second function D is applied thereto. In this exemplaryembodiment, the first item of calculated image information D_x is asegmentation of the training image x. In this exemplary embodiment, theisolated item of image information y is a segmentation of a trainingimage x1, x2, . . . , xN, wherein the training image x1, x2, . . . , xNassociated with the isolated item of image information is not identicalto the training image x. It is also possible to generate an artificialsegmentation.

In addition to the isolated segmentation y, in this exemplaryembodiment, the first function G furthermore obtains as an input arandom vector; this is not shown in FIG. 3, for clarity. In thisexample, the random vector is a random image, wherein the random imagehas the same number of pixels as the training images x1, x2, . . . , xN,and wherein each pixel is equally distributed between a minimum valueand a maximum value. However, other random images or random vectors arealso conceivable, for example normally distributed pixels. The firstfunction G generates a calculated image G_y from the random vector andthe isolated segmentation y. The second function D is applied to thiscalculated image G_y; in this exemplary embodiment, the second item ofcalculated image information D_G_y is a segmentation of the calculatedimage G_y.

In the exemplary embodiment depicted, the first calculated segmentationD_x is compared with the training-image information i by a firstcomparator unit C1, furthermore the second calculated segmentation D_G_yis compared with the isolated segmentation y by a second comparator unitC2. In this case, the comparator unit C1 calculates a first differenceε1, and the second comparator unit C2 calculates a second difference ε2.In the exemplary embodiment shown, the first difference ε1 between thefirst calculated segmentation D_x and the training-image information isdefined by the Sorensen-Dice coefficient SDC(D_x,i), where

${{SDC}\left( {x,y} \right)} = \frac{2\Sigma_{j}x_{j}y_{j}}{{\Sigma_{j}x_{j}^{2}} + {\Sigma_{j}y_{j}^{2}}}$

Instead of the squared pixel values in the denominator it is alsopossible to use the amounts of the pixel values. However, the form shownof the Sørensen-Dice coefficient has the advantage that the gradient ofthe cost function required for the backpropagation can be expressed as aclosed form expression. The second difference ε2 between the secondcalculated segmentation D_G_y and the isolated segmentation y is definedby “sum of squared differences” SSD(D_G_y,y) as

SSD(x,y)=Σ_(j)(x _(j) −y _(j))².

The differences ε1 and ε2 are then incorporated in the cost functionK_(D) of the second function D and/or the cost function KG of the firstfunction G. It is also possible for the differences ε1 and ε2 to useother functional relationships. The parameters of the first function Gand the second function D are then adjusted via minimization of the costfunctions K_(G) and K_(D) by backpropagation.

In the first exemplary embodiment and in the second exemplaryembodiment, depicted in FIG. 2 and FIG. 3, the training function TF isidentical to the first image-information-processing function G.

FIG. 4 shows a third exemplary embodiment of a method for thedetermination of a training function. The third exemplary embodimentpartially comprises the same elements as the first exemplary embodimentof the method as shown in FIG. 2, wherein reference is made to thedescription of the schematic depiction in FIG. 2 in respect of elementswhich remain the same. In the third exemplary embodiment, theimage-processing second function D and the image-information-processingfirst function G are each implemented by an autoencoder. In thisexample, both autoencoders comprise 5 node layers G1, . . . G5, D1, . .. D5, but they can also comprise another, odd, number of node layers. Inthis exemplary embodiment, the number of nodes in the central layer G3of the first function G is the same as the number of nodes in thecentral layer D3 of the second function D.

In the third exemplary embodiment depicted, a training function TF is tobe determined, wherein the training function TF determines from adefined segmentation y an associated image TF_y with this segmentationy. Therefore, the training-image information i and the isolated item ofimage information y can also be understood to be images, wherein a pixelcan only assume the values 0 and 1 and wherein the images have the samenumber of pixels as the training images x1, x2, . . . , xN.

In one training step, the second function D obtains a training image xfrom the set x1, x2, . . . , xN of training images as input data anddepicts it on an output image x′. In this depiction, the nodes of thecentral layer D3 have the second node values D3_x. The first function Gfurthermore obtains the image information i associated with the trainingimage x as input information and depicts this on the output informationi′. In this depiction, the nodes of the central layer G3 of the firstfunction G have the third node values G3_i. The first function Gfurthermore obtains a further isolated item of image information y asinput information and depicts this on the output information y′, whereinthe isolated item of image information y is selected from the set oftraining image information i1, i2, . . . iN and is different from thetraining-image information i. In this depiction, the nodes of thecentral layer G3 of the first function G have the first node valuesG3_y.

The distance of the training image x from the output image x′ and thedistance of the training-image information i from the image informationI′ make a positive contribution to the cost function. Alternatively, thedistance of the isolated item of image information y from the imageinformation y′ can also make a positive contribution. As a contributionto the cost function in each case the “sum of the pixel-related squareddifferences” (sum of squared differences is an English technical term;“SSD” for short) of the images x, x′ and the image information i, i′ andy, y′ is used. However, it is also possible to use other distance terms.

Furthermore, the distance of the central node values D3_x from thecentral node values G3_i makes a positive contribution to the costfunction; the distance of the central node values D3_x from the centralnode values G3_y makes a positive contribution. For the calculation ofthe distance, the node values D3_x, G3_i, G3_y are understood to bevectors in an n-dimensional vector space, wherein n corresponds to thenumber of nodes in the central layers D3 and G3 and the j-th value ofthe vector corresponds to the value of the j-th node in the centrallayer D3, G3. The distance of the node values is in each case understoodto be the sum of the squared differences of the vector components.However, other distance terms are also conceivable, in particulardistance terms from an L1-norm (Manhattan metric), from the maximum normor the minimum norm. The distances are determined by a comparisonfunction C3. The choice of the preceding sign of the contributions tothe cost function causes the second node values D3_x and the third nodevalues G3_i in the n-dimensional vector space to lie close to oneanother, while the second node values D3_x and the first node valuesG3_y in the n-dimensional vector space are far apart. This causes theimage x and the associated image information i of the image x to havesimilar edge values; non-associated image information y has differentedge values.

The cost function is then minimized by backpropagation by the adjustmentof the edge weights of the edges between the layers D1, D2, D3, D4, D5the second function D and the edges between the layers G1, G2, G3, G4,G5 of the first function G, and possibly the edge weights of the inputedges of the first function G and the second function D.

FIG. 5 is a schematic depiction of the training function TF in the thirdexemplary embodiment of a method for the determination of a trainingfunction. In this example, the training function TF is an artificialneural network ANN comprising the layers G1, G2, G3 of the firstfunction G and the layers D3, D4, D5 of the second function D. Herein,the edges between the layers G1 and G2, the layers G2 and G3, the layersD3 and D4 and the layers D4 and D5 with the respective weights areretained and furthermore the assignment between the isolated item ofimage information y and the input nodes of the layer G1 are retained.Furthermore, an edge with edge weight 1 is introduced between the j-thnodes of the central layer G3 and the j-th nodes of the central layer D3and the identity selected as the activation function of the nodes of thecentral layer D3 so that the value of the j-th node of the central layerD3 always corresponds to the value of the j-th node of the central layerG3 for all node pairs from the central layers G3, D3.

The training function constructed in this way now depicts any imageinformation y desired on an image TF_y containing the image informationy. In the exemplary embodiment shown, the image information y isgenerated by a generator unit IIG.

To determine an isolated segmentation y, it is possible to parameterizestructures that typically occur in the training images x1, x2, . . . ,xN or the training segmentations i1, i2, . . . , iN by geometric shapesand/or intensity distributions and therefore to determine as manyisolated segmentations y as desired. For example, for the identificationof breast cancer it is possible to describe vessels and mammary glandsas lines, dense regions as ellipses and cancer lesions as structureswith peaks. Cancer lesions can also be described by geometric structuresoriginating from a fractal growth process.

In FIG. 1 to FIG. 5, continuous arrows correspond to real images x, x′and calculated images G_r, G_y. Dashed arrows correspond to imageinformation y, i, random vectors r, calculated image information D_x,D_G_y, D_r and differences ε1, ε2 from image information. Dashed-dottedarrows correspond to node values G3_i, G3_y, D3_x of central layers G3,D3. Herein, the corresponding data is forwarded from the unit at thefoot of an arrow to the unit at the head of the arrow.

FIG. 6 shows an example of an image-processing artificial neural networkANN. An artificial neural network ANN consists of nodes N1.1, N1.2,N1.3, N2.1, N3.1, N3.2 and edges M1.1, M1.2, M2.1, M2.2, which start ata start node and and/or end at an end node. For example, the edge M2.1starts at the start node N1.1 and ends at the end node N2.1. In theexemplary embodiment shown, each node N1.1, N1.2, N1.3, N2.1, N3.1, N3.2and each edge M1.1, M1.2, M2.1, M2.2 is assigned a numerical value, inparticular a real number. The numerical value of an edge M1.1, M1.2,M2.1, M2.2 can be described as an edge weight. In this case, the valueof a node N1.1, N1.2, N1.3, N2.1, N3.1, N3.2 depends upon the edgeweights of all the edges which have the node as an end node and the nodevalues of the associated start node. In the exemplary embodiment shown,the value ki of a node i is calculated with the formula

$k_{i} = {F\left( {\sum\limits_{j \in {{EG}{(i)}}}\; {w_{ij}k_{j}}} \right)}$

wherein the sum runs over all nodes j, which are the start node of anedge with j as an end node and wherein ki is the value of the node j andwij is the edge weight of the edge between the nodes i and j. In thisexample, the function F is the tan h function, but other functions arealso possible, for example a step function. Other functionalrelationships between node values are also possible.

The artificial neural network ANN depicted in the exemplary embodimenthas multiple layers N1, N2, N3 of nodes, including an input layer N1, anoutput layer N3 and a concealed layer N2. The artificial neural networkANN can also comprise a plurality of concealed layers. The input layercomprises input nodes N1.1, N1.2, N1.3; the output layer comprisesoutput nodes N3.1, N3.2. In the exemplary embodiment shown, edges alwaysonly connect nodes in adjacent layers, wherein edges only point in onedirection (an English technical term for this is “feed forwardnetwork”). For example, each edge with a node from the layer N1 has anode from the layer N2 as an end node as a start node. However, it isalso possible for edges between non-adjacent layers, in particularwithin the same layer, to be present or for edges to extend in differentdirections. Furthermore, it is possible to use artificial neuralnetworks ANN without a layer structure.

The artificial neural network ANN depicted in FIG. 6 is animage-processing artificial neural network ANN containing an image x asan input. In this case, the intensity values of pixels x.1, x.2, x.3 aretransferred to the input nodes of the artificial neural network ANN. Forpurposes of simplification, FIG. 6 only depicts three input nodes N1.1,N1.2, N1.3 for three pixels x.1, x.2, x.3 depicted; an image-processingartificial neural network ANN generally has more input nodes, inparticular one input node for each pixel of the input image. Theartificial neural network ANN depicted in FIG. 6 comprises two outputnodes N3.1, N3.2, wherein the value of the node N3.1 corresponds to theprobability of the input image x originating from a set of trainingimages x1, x2, . . . , xN, and wherein the value of the node N3.2corresponds to the probability of a bone fracture being present in abone depicted in the image x. It is also possible for the artificialneural network ANN to have the same number of input and output nodes.Such an artificial neural network ANN is suitable for calculating asegmentation of an input image x.

FIG. 7 a flowchart of the first exemplary embodiment of the methoddepicted in FIG. 1 and the second exemplary embodiment of the methoddepicted in FIG. 2. Herein, it is also possible to use a differentsequence of method steps than that depicted.

The first step in the flowchart depicted is the first reception REC-1 ofa training image x and an item of training-image information i by meansof an interface FDU.1, wherein the item of training-image information iis image information for the training image x. Herein, the trainingimage x and the training-image information i are stored in a database TDcomprising a plurality of training images x1, x2, . . . , xN and itemsof training image information i1, i2, . . . , iN.

The next step in the flowchart depicted is the second reception REC-2 ofan isolated item of image information y by means of the interface FDU.1,wherein the isolated item of image information y is independent of thetraining image x. In the first exemplary embodiment, the isolated imagefunction is generated by a generator IIG; in the second exemplaryembodiment, the isolated item of image information y is one of the itemsof training image information i1, i2, . . . , iN from the database TD,wherein the isolated item of image information y is different from thetraining-image information i.

The next steps in the flowchart depicted are the first calculationCALC-1 of a calculated image G_y by the application of theimage-information-processing first function G to the isolated item ofimage information y by means of a computing unit FDU.2, the secondcalculation CALC-2 of a first items calculated image information D_x bythe application of an image-processing second function D to the trainingimage x by means of the computing unit FDU.2 and the third calculationCALC-3 of a second item of calculated image information D_G_y by theapplication of the second function D to the calculated image G_y bymeans of the computing unit FDU.2.

A further step entails the adjustment ADJ of a parameter of the firstfunction G and the second function D based on the first calculated imageinformation D_x and the second item of calculated image informationD_G_y (and hence based on the calculated image G_y) by means of thecomputing unit FDU.2. In the example depicted, the adjustment ADJ isperformed by minimizing a cost function by a stochastic gradient method(“stochastic gradient descent”) and backpropagation. Alternatively, itis also possible to use other algorithms with backpropagation, forexample an adaptive gradient method (“adaptive gradient descent”,“AdaGrad”) or an “adaptive moment estimation” (“AdaM” for short).However, it is also apply to apply other optimization methods, forexample, simulated cooling (“simulated annealing”) or the ant colonyalgorithm (“ant colony optimization”).

The last step in the method depicted is the determination DET of atraining function TF, wherein here the training function TF is identicalto the image-information-processing first function G.

FIG. 8 shows a flowchart of the third exemplary embodiment of the methoddepicted in FIG. 4. Herein, it is also possible to use a differentsequence of method steps than that depicted. In respect of the elementsREC-1 and REC-2 that remain the same, reference is made to thedescription of the flowchart in FIG. 7. In this exemplary embodiment,the first function G is an information autoencoder, the second functionD is an image autoencoder.

The next steps in the flowchart depicted are the first calculationCALC-1 of the first node values G3_y of the central layer G3 of thefirst function G when applied to the isolated item of image informationy by means of the computing unit FDU.2, the second calculation CALC-2 ofthe second node values D3_x of the central layer D3 of the secondfunction D when applied to the training image x by the processor FDU.2and the fourth calculation CALC-4 of third node values G3_i of thecentral layer G3 of the first function G when applied to thetraining-image information i.

A further step in this exemplary embodiment entails the adjustment ADJof a parameter of the first function G and the second function D basedon the first node values G3_y, the second node values D3_x and the thirdnode values G3_i by means of the processor FDU.2. The adjustment ADJ isperformed by minimizing a cost function by backpropagation. In thiscase, the distance between the first node values G3_y and the secondnode values D3_x reduces the cost function, the distance between thesecond node values D3_x and the third node values G3_i increases thecost function. Furthermore, in the exemplary embodiment shown, thedistances between the training image x and the output x′ of the secondfunction D, the training-image information i and the output i′ of thefirst function G make a positive contribution to the cost function. Itis also possible for the distance between the isolated item of imageinformation y and the output y′ of the first function G to make apositive contribution to the cost function.

A further step in the method depicted is the determination DET of thetraining function TF. Here, the training function consists of aconcatenation of the layers G1, G2, G3 of the first function G beforethe central layer G3 thereof with the layers D3, D4, D5 of the secondfunction D after the central layer D3 thereof, wherein the values of thecentral layer G3 are transferred to the values of the central layer D3.

FIG. 9 shows a function-determining computer FDU for the determinationof a training function TF. The function-determining unit shown here isconfigured to carry out a method according to the invention. Thisdetermining computer FDU comprises an interface FDU.1, a computing unitFDU.2, a storage unit FDU.3 and an input/output unit FDU.4.

The function-determining computer FDU can be a computer, amicrocontroller or an integrated circuit. Alternatively, thefunction-determining computer FDU can be a real group (“cluster”) orvirtual group (“cloud”) of computers. An interface FDU.1 can be ahardware or software interface (for example a PCI Bus, USB or Firewire).A processor FDU.2 can be formed by hardware or software elements, forexample a microprocessor or a so-called FPGA (“field programmable gatearray”). A storage unit FDU.3 can be implemented as a non-permanentworking memory (random access memory, RAM) or as a permanent mass memory(hard disk, USB stick, SD card, solid state disk). An input/output unitFDU.4 has at least one input unit and/or at least one output unit. Inthe exemplary embodiment, the input/output unit FDU.4 can be used toinput parameters of the method, such as the number of iterations, or tooutput information on the method, such as an average cost function, tothe user.

The storage unit FDU.3 can comprise the database TD with the trainingimages x1, x2, . . . , xN and the training image information i1, i2, . .. , iN, but the database TD can also be embodied separately from thefunction-determining unit FDU.

TABLE A Pseudocode for the first exemplary embodiment A.1G.init(G_params_initial) A.2 D.init(D_params_initial) A.3 a = 0.01 A.4for n from 1 to training_epochs: for each (tr_image, tr_info) intr_data: A.5 calc_info = generate_calc_info( ) A.6 calc_image =G.apply_to(calc_info, random_data) A.7 rs_tr = D.apply_to(tr_image) A.8D.params −= a*back_prop(D.prob_loss(1, rs_tr.prob) +D.info_loss(tr_info, res_tr.info)) A.9 rs_calc = D.apply_to(calc_image)A.10 D.params −= a*back_prop(D.prob_loss(0, rs_calc.prob)) A.11 rs_calc= D.apply_to(calc_image) A.12 G.params −=a*back_prop(G.prob_loss(rs_calc.prob))

Table A shows pseudocode for the first exemplary embodiment of themethod depicted schematically in FIG. 2. In the lines of code A.1 andA.2, the first function “G” and the second function “D” are initializedas artificial neural networks with standard parameters. The learningspeed “a” quantifies the speed of the learning process and isinitialized in line A.3.

In A.4, a loop with “trainings_epochs” iterations is defined via acounting variable “n” and a loop over all pairs of training images“tr_image” and training image information “tr_info” in the training data“tr_data”. Herein, “trainings_epochs” is a predefined whole numberdescribing the number of training cycles.

In the lines of code A.5 and A.6, an isolated item of image information“calc_info” is generated without recourse to the training imageinformation. Then, a calculated image “calc_image” is generated from theisolated item of image information “calc_info” and random numbers“random_data” by means of the first function “G”.

In the lines of code A.7 and A.9, the results “rs_tr” and “rs_calc” ofthe application of the image-processing second function “D” to thetraining image “tr_image” and the calculated image “calc_image” aredetermined. The results “rs_tr” and “rs_calc” comprises theprobabilities “rs_tr.prob” and “rs_calc.prob” of the training image“tr_image” and the calculated image “calc_image” being contained in thetraining data “tr_data”. Furthermore, the results “rs_tr” and “rs_calc”comprise the estimated probabilities “rs_tr.info” and “rs_calc.info” ofbone depicted in the image having a fracture.

In the lines of code A.8 and A.10, the parameters “D.param” of thesecond function “D” are adjusted in that the cost function is minimizedby means of backpropagation. These lines of code should be understood asmeaning that the function “back_prob” contains not only the values ofthe cost function as input but also the values of the partialderivatives of the cost function required for the for thebackpropagation. In the line of code A.8, the cost function is made upof the component “prob_loss”, which measures the difference of theprobability of an image being containing in the training data, and thecomponent “info_loss”, which measures the difference between theestimated image information and the actual image information. Herein,the use of the additive component “info_loss” is optional. In theexemplary embodiment shown, both parts of the cost function are definedby binary cross entropy but obviously other functional relationships forthe cost function are conceivable, for example quadratic distance.

In the lines of code A.11, the result “rs_calc” is calculated again byapplying the second function “D” to the calculated image “calc_image”.Herein, the result “rs_calc” can differ from the probability calculatedin A.9 since in line of code A.10 the parameters of the first function“D” were adjusted. In the line of code A.12, the cost function“G.prob_loss” is calculated and the parameters “G.param” of the firstfunction “G” are adjusted by backpropagation of the cost function takingaccount of the learning speed “a”.

In this exemplary embodiment, a logarithmic cost function defined asG.loss(x)=log(x) is used as a cost function of the first function “G”.However, other cost functions are also conceivable which have the resultthat the value of the cost function is maximum in the interval between 0and 1 for x=1.

TABLE B Pseudocode for the second exemplary embodiment B.1G.init(G_params_initial) B.2 D.init(D_params_initial) B.3 a = 0.01 B.4for n from 1 to training_epochs: for each (tr_image, tr_mask) intr_data: B.5 other_mask = choose_other_info(tr_data, tr_mask) B.6calc_image = G.apply(other_mask, random_data) B.7 pred_mask_tr =D.apply(tr_image) B.8 D.params −= a*back_prop(D.loss(pred_mask_tr,tr_mask)) B.9 pred_mask_calc = D.apply(calc_image) B.10 D.params −=a*back_prop(D.loss(pred_mask_calc, tr_mask)) B.11 pred_mask_calc =D.apply(calc_image) B.12 G.params −= a*back_prop(G.loss(pred_mask_calc,tr_mask))

Table B depicts pseudocode implementing the second exemplary embodimentof the method depicted in FIG. 3. In the pseudocode, the work isperformed with two-dimensional images, but it is also applicable toimages with three and more dimensions. The image information for atwo-dimensional image is a mask marking a region of interest in theimage, for example a defined organ.

As before, a two-dimensional image can be described by a collection ofN×M pixels with an intensity value or grey tone. Therefore, an image issimultaneously a vector in an M·N-dimensional vector space.

A mask of this image, or equivalently the segmentation of a region ofinterest is then also an image with N×M pixels, wherein the value of apixel is 0 if the pixel lies outside the region of interest and whereinthe value of a pixel is 1 if the pixel is within the region of interest.Therefore, a mask can also be described as a vector in anM·N-dimensional vector space, wherein the vector only has the entries 0and 1.

The lines of code B.1 to B.4 correspond to the lines of code A.1 to A.4in Table A. Here, the training data consists of a training image“tr_image” and an associated mask “tr_mask”. In the line of code B.5, anisolated segmentation “other_mask”, which is not identical to thetraining segmentation “tr_mask”, is selected from the training data“tr_data”.

In the line of code B.6, the first function “G” generates a calculatedimage “calc_image” from the mask “tr_mask” and additional random data.In line B.7, the second function “D” determines a mask “pred_mask_tr”from the training image “tr_image” and, in line B.8, parameters“D.params” of the second function “D” are adjusted by backpropagation ofthe difference cost function between “pred_mask_tr” and “tr_mask”.Furthermore, in line B.9, the second function “D” determines a mask“pred_mask_synth” from the calculated image “calc_image” and, in lineB.10, parameter “D.params” of the second function “D” are adjusted bybackpropagation of the difference cost function between“pred_mask_synth” and “tr_mask”.

In the lines of code B.11 and B.12, a mask “pred_mask_tr” is calculatedagain from the training image “tr_image” by means of the ANN “D”,wherein here this produces another mask since the parameters “D.params”of the first function “D” have changed and the parameters “G.params” ofthe first function “G” have been adjusted by backpropagation of thedifference cost function between “pred_mask_synth” and “tr_mask”.

Any distance function that calculates the distance of the two inputs xand y can be used as the cost function “loss(x,y)” of theimage-processing second function “D” and theimage-information-processing first function “G”. In this exemplaryembodiment, the Sorensen-Dice coefficient SD(x,y) of a vector or a maskx and a vector or mask y is used for the cost function of theimage-processing second function D. In this exemplary embodiment, the“sum of squared differences” (an English technical term; SSD for short)of two entries x and y is used as the cost function of theimage-information-processing first function G.

TABLE C Pseudocode for the third exemplary embodiment C.1G.init_autoencoder(G_params_initial) C.2D.init_autoencoder(D_params_initial) C.3 a = 0.01 C.4 for n from 1 totraining_epochs: for each (tr_image, tr_info) in tr_data: C.5 other_info= choose_other_info(tr_data, tr_info) C.6 tr_image_rcstr =D.apply(tr_image) C.7 loss_D = distance(tr_image, tr_image_rcstr) + regC.8 tr_info_rcstr = G.apply(tr_info) C.9 loss_G = distance(tr_info,tr_info_rcstr) + reg C.10 nodes_tr_image = D.central_nodes(tr_image)C.11 nodes_tr_info = G.central_nodes(tr_info) C.12 nodes_other_info =G.central_nodes(other_info) C.13 loss_nodes = distance(nodes_tr_image,nodes_tr_info) − distance(nodes_tr_image, nodes_other_info) C.14D.params −= a*back_prop(loss_D + loss_G + loss_nodes) C.15 G.params −=a*back_prop(loss_D + loss_G + loss_nodes)

The effect of lines of code C.1 to C.4 corresponds to that of the linesof code A.1 to A.4. Here, the variable “G” is an informationautoencoder, here the variable “D” is an image autoencoder. In line ofcode C.5, an isolated item of image information “other_info” that doesnot correspond to the training-image information “tr_info” is selectedfrom the training data “tr_data”.

In the lines of code C.6 and C.7, the image autoencoder “D” is appliedto the training image “tr_image” in order to generate a reconstructedtraining image “tr_image_rcstr”. The contribution “loss_D” of the imageautoencoder “D” to the total cost function is calculated from thedistance between the training image “tr_image” and the reconstructedtraining image “tr_image_rcstr”. The calculation includes the additionof a term “reg” for regularization, which is dependent upon theparameters of the image autoencoder “D” and in particular can becalculated as the L2-norm for all parameters, in particular the edgeweights, of the image autoencoder “D”.

In the lines of code C.8 and C.9, the information autoencoder “G” isapplied to the training-image information “tr_info” in order to generatea reconstructed training-image information “tr_info_rcstr”. Thecontribution “loss_G” of the image autoencoder “G” to the total costfunction is calculated from the distance between the training-imageinformation “tr_info” and the reconstructed training-image information“tr_info_rcstr”. The calculation includes the addition of a term “reg”for regularization, which is dependent upon the parameters of the imageautoencoder “G” and in particular can be calculated as the L2-norm forall parameters, in particular the edge weights, of the image autoencoder“G”.

In the lines of code C.10 to C.12, the values “nodes_tr_image”,“nodes_tr_info” and “nodes_other_info” of the central nodes of theautoencoders “D” and “G” are calculated when applied to the trainingimage “tr_image”, the training-image information “tr_info” and theisolated item of image information “other_info”. The contribution“loss_nodes” to the total cost function is obtained in line of code C.13as the difference between the distance of the node values“nodes_tr_image” and “nodes_tr_info” and the distance of the node values“nodes_tr_image” and “nodes_other_info”. It is optionally possible forother factors to be included in this calculation.

The backpropagation of the total cost function made up of the sum of thecontributions “loss_D”, “loss_G” and “loss_nodes” results in theadjustment of the parameters of the autoencoders “D” and “G” in thelines of code C.14 and C.15.

Although modifications and changes may be suggested by those skilled inthe art, it is the intention of the Applicant to embody within thepatent warranted hereon all changes and modifications as reasonably andproperly come within the scope of the Applicant's contribution to theart.

1. A method for determining a training function comprising: in acomputer, receiving a first reception of a training image and an item oftraining-image information via an interface of the computer, wherein thetraining-image information is image information for the training image;in said computer, receiving a second reception of an isolated item ofimage information via the interface, wherein the isolated item of imageinformation is independent of the training image; in said computer,making a first calculation of a first result of animage-information-processing first function when applied to the isolateditem of image information; in said computer, making a second calculationof a second result of an image-processing second function when appliedto the training image; in said computer, making an adjustment of aparameter of at least one of the image-information-processing firstfunction or the image-processing second function, based on at least thefirst result and the second result; and in said computer, making adetermination of a training function based on theimage-information-processing first function wherein, when applied to anitem of image information, the training function generates an associatedimage as an output of the computer.
 2. The method as claimed in claim 1,wherein at least one of the first function or the second function isprovided by an artificial neural network, and wherein the parameters ofthe artificial neural network comprise edge weights of the artificialneural network.
 3. The method as claimed in claim 2, wherein theadjustment comprises adjusting the edge weights by minimizing a costfunction by execution of backpropagation.
 4. The method as claimed inclaim 1, wherein said item of image information of an image comprisessegmentation of the image into at least one image region.
 5. The methodas claimed in claim 1, wherein said item of image information of animage comprises a variable that assesses whether a defined object or aplurality of defined objects are depicted in the image.
 6. The method asclaimed in claim 1, wherein the image-information-processing firstfunction is a generator function that, when applied to said item ofimage information, generates an associated image as said output; whereinthe image-processing second function is a classification function that,when applied to said image, generates an associated item of imageinformation as said output; wherein the first result is a calculatedimage; wherein the second result is a first item of calculated imageinformation; wherein the training function is theimage-information-processing first function; and wherein the methodfurthermore comprises making a third calculation in the computer of asecond item of calculated image information by applying theimage-processing function to the calculated image.
 7. The method asclaimed in claim 6, wherein the first item of calculated imageinformation comprises an estimation of a first probability of thetraining image being contained in a set of training images; and whereinthe second item of calculated image information comprises an estimationof a second probability of the calculated image being contained in a setof training images.
 8. The method as claimed in claim 7, wherein atleast one of the first function or the second function is provided by anartificial neural network, and wherein the parameters of the artificialneural network comprise edge weights of the artificial neural network;wherein the adjustment comprises adjusting the edge weights byminimizing a cost function by execution of backpropagation; and whereinthe cost function is based on at least a first difference of the firstitem of calculated image information from the training-imageinformation.
 9. The method as claimed in claim 8, wherein the costfunction is furthermore based on at least a second difference of thesecond item of calculated image information from the isolated item ofimage information.
 10. The method as claimed in claim 1, wherein atleast one of the first function or the second function is provided by anartificial neural network, and wherein the parameters of the artificialneural network comprise edge weights of the artificial neural network;wherein the image-information-processing first function is aninformation autoencoder that, when applied to said first item of imageinformation, generates a second item of image information as saidoutput; wherein the image-processing second function is an imageautoencoder that, when applied to a first image, generates a secondimage as said output; wherein a central layer of the informationautoencoder and a central layer of the image autoencoder have a samenumber of central nodes; wherein the first result corresponds to firstnode values; wherein the first node values are values of the nodes ofthe central layer of the information autoencoder when the isolated itemof image information is the input value of the information autoencoder;wherein the second result corresponds to second node values; wherein thesecond node values are values of the nodes of the central layer of theimage autoencoder when the training image is the input value of theimage autoencoder; wherein the method furthermore comprises making afurther calculation of further node values that are values of the nodesof the central layer of the information autoencoder when thetraining-image information is the input value of the informationautoencoder; and wherein the training function is composed of the firstpart of the information autoencoder with the second part of the imageautoencoder.
 11. The method as claimed in claim 10, wherein at least oneof the first function or the second function is provided by anartificial neural network, and wherein the parameters of the artificialneural network comprise edge weights of the artificial neural network;wherein the adjustment comprises adjusting the edge weights byminimizing a cost function by execution of backpropagation; and whereina distance between the first node values and the second node valuesmakes a negative contribution to the cost function and wherein adistance between the second node values and the third node values makesa positive contribution to the cost function.
 12. The method as claimedin claim 10, wherein: the training function generates an image as anoutput value from an item of image information as an input value suchthat an item of image information is used as an input value of theinformation autoencoder; the node values of the central layer of theinformation autoencoder are transferred to the node values of thecentral layer of the image autoencoder; and the output value of thetraining function corresponds to the resulting output value of the imageautoencoder.
 13. A function-determining computer comprising: aprocessor; an interface that receives a first reception of a trainingimage and an item of training-image information into said processor,wherein the training-image information is image information for thetraining image; said interface also receiving a second reception of anisolated item of image information into said processor, wherein theisolated item of image information is independent of the training image;said processor being configured to make a first calculation of a firstresult of an image-information-processing first function when applied tothe isolated item of image information; said processor being configuredto make a second calculation of a second result of an image-processingsecond function when applied to the training image; said processor beingconfigured to make an adjustment of a parameter of at least one of theimage-information-processing first function or the image-processingsecond function, based on at least the first result and the secondresult; and said processor being configured to make a determination of atraining function based on the image-information-processing firstfunction wherein, when applied to an item of image information, thetraining function generates an associated image as an output of thecomputer.
 14. A non-transitory, computer-readable data storage mediumencoded with programming instructions, said storage medium being loadedinto a computer and said programming instructions causing said computerto: receive a first reception of a training image and an item oftraining-image information via an interface of the computer, wherein thetraining-image information is image information for the training image;receive a second reception of an isolated item of image information viathe interface, wherein the isolated item of image information isindependent of the training image; make a first calculation of a firstresult of an image-information-processing first function when applied tothe isolated item of image information; make a second calculation of asecond result of an image-processing second function when applied to thetraining image; make an adjustment of a parameter of at least one of theimage-information-processing first function or the image-processingsecond function, based on at least the first result and the secondresult; and make a determination of a training function based on theimage-information-processing first function wherein, when applied to anitem of image information, the training function generates an associatedimage as an output of the computer.